This paper is a survey on Machine learning approaches in terms of classification, regression, and clustering. Data Science Journal 18 (1): 14. Gladstone RJ, Nabian MA, Keshavarzzadeh V, Meidani H (2021) Robust topology optimization using variational autoencoders. Caroline Glackin on LinkedIn: Using the power of machine learning in DOI: https://doi.org/10.17485/ijst/2015/v8iS7/70151. (2014)] as shown in Fig. Intelligent techniques for deception detection: a survey and critical Survival analysis is used to analyze data in which the time until the event is of interest (Kartal 2015). Predicting Student Dropout in Higher Education. Finding and implementing solutions to this problem has implications well beyond the benefits to individual students. Comput Methods Appl Mech Eng 374:113575, Lynch ME, Sarkar S, Maute K (2019) Machine learning to aid tuning of numerical parameters in topology optimization. Mgala, M and Mbogho, A. 3https://www.linkedin.com/pulse/ai-classroom-machine-learning-education-michael-s-davison-iii. In: Proceedings of Eighth International Conference on Scientific and Statistical Database Systems, p. 4. 2020 Sep 24;20(19):5476. doi: 10.3390/s20195476. Fernndez-Godino MG, Park C, Kim NH, Haftka RT (2016) Review of multi-fidelity models. School dropout is absenteeism from school for no good reason for a continuous number of days. PubMedGoogle Scholar. Halland, R, Igel, C and Alstrup, S. 2015. UNESCO. Prior to running a K-means clustering algorithm, the machine learning algorithm needs to know how many clusters it should produce in the end (i.e., K). Values of the AUC statistic that are close to 0.5 indicate very poor fitting classification models, while values that are higher and closer to 1 indicate more accurate classification models. This includes both traditional machine learning algorithms that learn patterns and identify new relationships from the data and thereby make predictions as well as AI capable of learning in. J Syst Softw 167:110617, Asperti A, Evangelista D, Piccolomini EL (2021) A survey on variational autoencoders from a green AI perspective. The technical interpretation of the AUC and ROC curve statistic is the probability that the classification model will rank a randomly chosen Yes case higher than a randomly chosen No case. With traditional CPUs, ANNs were expensive in terms of computational time to train the network, but the invention of cloud computing and increased computing power have relieved the computational burden. Aerosp Sci Technol 92:722737, Tenne Y (2019). One of the first applications of machine learning in education had been helping quizzes and tests move from multiple choice to fill in the blank answers.2 The evaluation of students free form answers was based on Natural Language Processing (NLP) and machine learning. Babu, AR. Economic Effects of Student Dropouts: A Comparative Study. J Comput Phys 354:493511, Chandrasekhar A, Suresh K (2021) TOuNN: Topology optimization using neural networks. KDD, 19091918. 950957). High-School Dropout Prediction Using Machine Learning: A Danish Large-scale Study. CEUR Workshop Proceedings, 1828: 5359. arXiv preprint arXiv:1609.07196. Adv Sci 7(5):1902607, Chen X, Chen X, Zhou W, Zhang J, Yao W (2020) The heat source layout optimization using deep learning surrogate modeling. Eng Struct 155:91101, Fisher RA (1936) The use of multiple measurements in taxonomic problems. DOI: https://doi.org/10.3390/a7040538. 2013. Optim Lett 14(4):9891010. Briefings in Bioinformatics, 14(1): 1326. . What are Machine Learning Methods? - Survey Practice Mach Learn 42(3):287320, Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. The findings of these studies are very useful on understanding the problem and improving measures to address solution. Sensors (Basel). Since VAE is based on a probabilistic model, it has computational flexibility. Recently, approaches such as matrixvector multiplication [Gardner et al. Bookshelf HHS Vulnerability Disclosure, Help 2, No. Issue IJCCT 1(2), 106 (2010). Publish or Perish, available from https://harzing.com/resources/publish-or-perish, Hasegawa K, Fukami K, Murata T, Fukagata K (2020) CNN-LSTM based reduced order modeling of two-dimensional unsteady flows around a circular cylinder at different Reynolds numbers. 266275Cite as, Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT,volume 46). In: 2018 International Joint Conference on Neural Networks (IJCNN) (pp. Aulck, L, Velagapudi, N, Blumenstock, J and West, J. AIAA J 58(3):13041319, Hou TY, Lam KC, Zhang P, Zhang S (2019) Solving Bayesian inverse problems from the perspective of deep generative networks. IEEE Internet Things J. One of the simplest examples of a tuning parameter comes from K-means clustering. Among the ML methods listed in Fig. PMLR. Careers. In the context of classification, since ANN works by splitting the problem into layered networks of simpler elements, ANNs are reliable when the tasks involve many features. Armed with these predicted classifications, researchers and practitioners can tailor the survey experience in an attempt to mitigate the negative consequences of nonresponse or item missingness. As a result, better algorithms and more powerful networks have been developed. 2004. However, cost and time must be acquired to accommodate the process. Wang, W, Yu, H and Miao, C. 2017b. Survey of review spam detection using machine learning techniques Jobs -. (2016)] have been developed to reduce the amount of computation when the data set is more than 100k. In the 1940s, [McCulloch and Pitts (1943)] formulated the first NN model. 32483253). Reducing student dropout rates is one of the challenges facing in the education sector globally. Kambampati S, Du Z, Chung H, Kim HA, Jauregui C, Townsend S, Hedges L (2018). Furthermore, GP can quantify the uncertainty about the prediction and have conditional distributions that allow adaptive sampling or Bayesian studies. Surveys are one of the most used methods in market research and data collection. 2015. Nakamura K, Suzuki Y (2020) Deep learning-based topological optimization for representing a user-specified design area. Struct Multidisc Optim 64(6):34733487, Bishop CM (1995) Neural networks for pattern recognition. Data from a large dimensional space is transformed into a smaller dimensional space ensuring that it provides similar information. Dmitry Namiot, Eugene Ilyushin and Ivan Chizov00:28:17 A Survey of Model Inversion Attacks a. 5http://www.research.ibm.com/cognitive-computing/machine-learning-applications/decision-support-education.shtml. arXiv preprint arXiv:2004.05461. arXiv preprint arXiv:2002.01927. . CNNs need large data for training and hence are computationally intensive. It is usually utilized in the linear regression framework while using the Gaussian kernel as the basis function. J Mech Des 141(11), Pnek D, Orosz T, Karban P (2020) Artap: Robust design optimization framework for engineering applications. Google Scholar. Poh, N and Smythe, I. [2211.00047] Optimizing machine learning methods to discover strong In this case, the main interest is not on modeling an outcome based on multiple independent variables, as in regression, but rather on understanding if there are combinations of variables (e.g., demographics) that can segment or group sets of customers, respondents or members of a group, class, or city. MOOC platforms such as Coursera and edX is among popular used platforms for generating datasets to be used in student dropout prediction (Chen et al., 2017). Hu, Q and Rangwala, H. 2017. Int J Num Methods Eng. It is a methods paper. 2014 IEEE Global Humanitarian Technology Conference South Asia Satellite, GHTC-SAS 2014, 251254. Deep Learning for Causal Inference. This approach captures time-varying factors and leverage those information to provide more accurate prediction of student dropout, using the dataset of students enrolled at Wayne State University (WSU) starting from 2002 until 2009. Recurrent neural networks (RNN) are an example of semi-supervised learning techniques. Review of Protocol Stack Development of Flying Ad-hoc Networks for Disaster Monitoring Applications. A case study in colleges in South Africa. Architecture of VAE (Asperti et al. Caldeira J, Nord B (2020) Deeply uncertain: Comparing methods of uncertainty quantification in deep learning algorithms. SIAM J Sci Comput 43(2):A1134A1162. 2014. arXiv preprint arXiv:1808.07440. In a similar vein, several projects were conducted including a project that aims to develop a prediction model that can be used by educators, schools, and policy makers to predict the risk of a student to drop out of school.4 Springboarding from these examples, IBMs Chalapathy Neti shared IBMs vision of Smart Classrooms using cloud-based learning systems that can help teachers identify students who are most at risk of dropping out, and observe why they are struggling, as well as provide insight into the interventions needed to overcome their learning challenges.5. The result of learning can be used for estimation, prediction, and classification. The objective function of GAN consists of a discriminator output for real data, and a discriminator output for generated fake data. Sorry, something went wrong. A Machine Learning Framework to Identify Students at Risk of Adverse Academic Outcomes. Processes 8(9):1170, McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. AIAA J 51(6):12841295. Burnap A, Pan Y, Liu Y, Ren Y, Lee H, Gonzalez R, Papalambros PY (2016b) Improving design preference prediction accuracy using feature learning. (2016) developed a survival analysis framework with the aim of identifying at-risk students using Cox proportional hazards model (Cox) and applied time-dependent Cox (TD-Cox). Outline of machine learning - Wikipedia A Survey of Machine Learning Approaches and Techniques for Student Dropout Prediction. Oh S, Jung Y, Kim S, Lee I, Kang N (2019) Deep generative design: Integration of topology optimization and generative models. A Novel Method to Improve Inter-Clinician Variation in the Diagnosis of Comput Mech 66:449469. Nano Lett 19(8):53665372, Jiang J, Fan JA (2020) Simulator-based training of generative neural networks for the inverse design of metasurfaces. 2015. Struct Multidisc Optim 61(4):14171438, Tao J, Sun G (2019) Application of deep learning based multi-fidelity surrogate model to robust aerodynamic design optimization. In: Proceedings of IEEE Second International Conference on Intelligent Computing and Control Systems, p. 261 (2018), Dayan, P.: Helmholtz machines and wake-sleep learning. Shahidul, SM and Karim, AHMZ. 2015. 17). This latent vector is used to predict an input image, and VAE training is performed with the goal of reducing the difference between the generated image and the input image as shown in Fig. Struct Saf 68:97109, Sun H, Ma L (2020) Generative design by using exploration approaches of reinforcement learning in density-based structural topology optimization. arXiv preprint arXiv:1910.14220. In: Advances in Neural Information Processing Systems, p. 69 (2003), Hehui, Q., Zhiwei, Q.: Feature selection using C4.5 algorithm for electricity price prediction. Mathematics | Free Full-Text | Survey on the Application of Artificial A Survey on Machine Learning Techniques with Applications Bagging, boosting and ensemble methods. Int J Mach Learn Cybern 11(7):13591385, Solanki KN, Acar E, Rais-Rohani M, Horstemeyer MF, Steele WG (2009) Product design optimisation with microstructure-property modelling and associated uncertainties. Therefore, developing countries research should focus on facilitating a more robust and comprehensive early warning systems for students dropout which can identify students at risk in future cohorts (early warning mechanism), rank students according to their probability of dropping (ranking mechanism) and identifying students who are at risk even before they drop (forecasting mechanism). J Mech Des 141(11):114502, Ma SB, Kim S, Kim JH (2020) Optimization design of a two-vane pump for wastewater treatment using machine-learning-based surrogate modeling. American Society of Mechanical Engineers. Therefore, developing countries need to focus on facilitating a more robust and comprehensive early warning systems for students dropout. Such variables are said to have ex-ante availability. Moreover, there exist quite a significant body of literature on machine learning based approaches associated with fighting dropouts (Sales et al., 2016; Lakkaraju et al., 2015; Ameri et al., 2016). DOI: https://doi.org/10.1186/s13673-016-0083-0. : Ensemble methods: foundations and algorithms. Struct Multidisc Optim 47(4):479491, Sosnovik I, Oseledets I (2019) Neural networks for topology optimization. NNs are typically black box approaches. Napier N, Sriraman SA, Tran HT, James KA (2020) An artificial neural network approach for generating high-resolution designs from low-resolution input in topology optimization. Educational data mining for prediction of student performance using clustering algorithms. Journal of Information and Data Management, 7(2): 166180. These models are constructed to maximize explanatory power (e.g., percentage of observed variance explained) and proper specification to minimize bias while also being attentive to parsimony. Survival Analysis Based Framework for Early Prediction of Student Dropouts. Introduction to Supervised Learning. ICIDCA 2019. Article, 1(1): 38. Information Sciences, 250: 113141. 2021 Jul 21;21(15):4953. doi: 10.3390/s21154953. Interpretability Methods in Machine Learning: A Brief Survey Google Scholar, Bostanabad R, Chan YC, Wang L, Zhu P, Chen W (2019) Globally approximate gaussian processes for big data with application to data-driven metamaterials design. Odonkor P, Lewis K (2019) Data-driven design of control strategies for distributed energy systems. IEEE (2008), Sri Mata Vaishmo Devi University, Katra, India, You can also search for this author in Furthermore, the paper highlights open challenges for future research directions. Designs 4(2):10, Sun G, Wang S (2019) A review of the artificial neural network surrogate modeling in aerodynamic design. The limitation of public datasets from developing countries (Mgala and Mbogho, 2015), brought the need to develop more datasets from different geographical location. Annal Stat 25:11891232, Fukushima K (1988) Neocognitron: A hierarchical neural network capable of visual pattern recognition. DOI: http://doi.org/10.5334/dsj-2019-014, 1. At the same time, the findings from this systematic survey and meta-analysis have significant policy implications for evaluation and monitoring. ESANN 2015 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, (April): 2224. J Franklin Inst 350(2):300317, Meng X, Karniadakis GE (2020) A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems. GAN is difficult to apply to various fields due to unstable learning ability; consequently, a DCGAN [Radford et al. J Roy Stat Soc: Ser B (methodol) 20(2):215232, MathSciNet Struct Multidisc Optim 39(3):311325, Adeli H, Park HS (1995) A neural dynamics model for structural optimizationtheory. Based on the application, there are many activation functions used in ANN, namely sigmoid, hyperbolic tangent, rectifier linear unit (ReLU), Heaviside, signum, and softmax functions [Karlik and Olgac (2011)]. Therefore, identifying at risk schools will help the authorities to plan for resource allocation before the risk. The authors declare no conflict of interest. Int Commun Heat Mass Transfer 109:104368. Appl. It's free to sign up and bid on jobs. IEEE Access 7:40564066, Tripathy RK, Bilionis I (2018) Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification. However, analysis of open-ended survey data is hard . J Mech Des 138(7):071404. Owing to the advancement of the amount of data collected, machine learning techniques have been applied to improve educational quality including areas related to learning and content analytics (Lan et al., 2014; Waters et al., 2014), knowledge tracing (Yudelson et al., 2013), learning material enhancement (Rakesh et al., 2014) and early warning systems (Beck and Davidson 2016; Brundage, 2014; US Department of Education, 2016). ASME J Risk Uncertainty Part B 6(4):041006, Gomes GSDS, Ludermir TB (2013) Optimization of the weights and asymmetric activation function family of neural network for time series forecasting. Many of these statistics can be extended to the case of more than two levels in the target variable of interest. In Bridge Optimization-Inspection and Condition Monitoring. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. The fundamental idea in linear regression is to find the coefficients of the basis functions that best model the data. Struct Optim 1(4):193202. Oh S, Jung Y, Lee I, Kang N (2018) Design automation by integrating generative adversarial networks and topology optimization. The deep RL can be employed where there exists a complex state and very high computations are required (Fig. Surv. Data-driven Intervention-level Prediction Modeling for Academic Performance. https://doi.org/10.1007/s00158-022-03369-9, DOI: https://doi.org/10.1007/s00158-022-03369-9. However, researchers began to focus on more complex problems and used more layers to train on large sets of data, resulting in longer computational times with multiple training iterations. Lei X, Liu C, Du Z, Zhang W, Guo X (2019) Machine learning-driven real-time topology optimization under moving morphable component-based framework. Therefore, when larger training data is used, the DNN can provide accurate predictions compared to classical ML algorithms where the accuracy is kept fairly constant. DOI: https://doi.org/10.1109/WAINA.2015.114, Li, Y, Wang, J, Ye, J and Reddy, CK. Durairaj, M and Vijitha, C. 2014. 13 trains a model that samples a latent vector from a simple distribution and generates it as an image based on the game-theoretic approach. DNN is created when NNs are stacked one after the other. Furthermore, to the knowledge of researchers, there are only few researches which has been conducted in developing countries. Third, many researches focus on providing early prediction rather than including ranking and forecasting mechanisms on addressing the problem of student dropout. Abstract - Many works in biomedical computer science research use machine learning techniques to give accurate results. Neural Netw 1(2):119130, Garca-Segura T, Yepes V, Frangopol DM (2017) Multi-objective design of post-tensioned concrete road bridges using artificial neural networks. Machine learning methods are beginning to be used for various aspects of survey research including responsive/adaptive designs, data processing and nonresponse adjustments and weighting. The study conducted by Mgala (2016) used the primary education data collected in Kenya, although the dataset is not publicly available. The authors declare that they have no conflict of interest. ICCSE 2016 11th International Conference on Computer Science and Education (ICCSE): 5257. Alotaibi FM, Al-Dhaqm A, Al-Otaibi YD, Alsewari AA. One of the most common unsupervised methods with which social scientists and market researchers might have some familiarity is hierarchical cluster analysis also known as segmentation. Wang F, Song M, Edelen A, Huang X (2019) Machine learning for design optimization of storage ring nonlinear dynamics. 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